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Multiscale Image Analysis with Stochastic Texture Differences

Abstract : We introduce a feature descriptor based on stochastic differences between random paths in spatially extended data, like an image. This descriptor behaves like a "texture gradient" and it is sensitive to both spatial and data scales. Applications to inferring characteristic scales in remotely sensed data is presented, together with multi-scale image analysis, and color edge-detection. The method is generic and can be applied to other types of vector or structured data.
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Contributor : Nicolas Brodu Connect in order to contact the contributor
Submitted on : Monday, March 23, 2015 - 10:55:22 AM
Last modification on : Thursday, January 20, 2022 - 5:26:33 PM


  • HAL Id : hal-01120159, version 1



Nicolas Brodu, Hussein Yahia. Multiscale Image Analysis with Stochastic Texture Differences. Recent Advances in Electronics and Computer Engineering, Feb 2015, Roorkee, India. ⟨hal-01120159⟩



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